MATERIALS AND METHODS

High-frequency oscillations in distributed neural networks reveal the dynamics of human decision making

Subjects

Ten healthy subjects (aged 26–44, 4 females) gave their written informed consent to participate in the experiments. All procedures were approved by the UCSF Committee on Human Research and conducted in accord-ance with the Declaration of Helsinki.

Recordings

The participants were seated in a magnetically shielded room, while their MEG was recorded with a 275 channel whole-head CTF Omega 2000 system (VSM MedTech, Coquitlam, BC, Canada), using a sampling rate of 1200 Hz.

Experimental paradigm

The subjects were asked to perform two-alternative forced choice tasks according to 5 different experimental conditions indicated in Table 1.

Choosing according to external cues was studied by letting the partici-pants choose between 2 visually presented single digit numbers, either the number that was printed in bold (Visual condition) or the number with the higher numerical value (Numerical condition). The processing of mem-ories during a decision task was investigated with an explicit memory retrieval paradigm: the participants were instructed to choose the number that they had memorized 12–24 hours before the main experiment on a list of 5 single digit numbers (Memory condition). The neural integration of preferences and internal values into a decision and action was ana-lyzed by asking the participants to choose the number that they personally preferred (Preference condition). In order to study the neural processing during choices that are completely independent of external variables and determined only by the agent’s current internal state, we confronted the participants with 2 exactly identical numbers (Free condition).

Each task condition consisted of 100 trials (in total 500 trials per sub-ject) with a visual stimulus followed by a button response (Figure 1A).

During each trial, subjects had to fi xate a cross in their central visual fi eld, which was presented 0.2 seconds after a 1 kHz warning tone. After an interval varying randomly between 1.2 and 1.4 seconds, the fi xation cross disappeared and two single digit numbers were projected 1.5° to the left and right of the subject’s central visual fi eld, respectively. Subjects were asked to choose one of the two numbers by pressing a button on the corresponding side with their right (dominant) index or middle fi nger within a time interval of 1.2 seconds. Each trial ended with a 750 Hz tone signaling the possibility to blink within the next 1.2 seconds (before the next warning tone). Motor responses were obtained from the right hand only, because we were interested in the cognitive aspects of decision making rather than movement selection, and therefore wanted to ensure a constant motor response in all conditions and trials. Congruency effects due to responses to visual stimuli with the ispilateral vs. contralateral hand would have been equally distributed across conditions.

Conditions 1–4 (Table 1) were presented in 4 recurring blocks of 25 trials each, with a possibility to rest between the blocks as long as considered necessary by the subject. Trials of the Free condition were randomly interleaved with the other conditions in order to prevent the subjects from making their decision before the stimuli were actually presented. In addition, subjects were instructed to make their choices

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3 spontaneously in each Free trial rather than according to preselected

pat-terns, and to press each button overall in about 50% of cases. Incorrect responses given in conditions 1–3 were excluded from further analysis.

The sequence of digits presented in each condition was randomized for each subject by a computer.

Behavioral analyses

The response latency was determined for each trial. The randomness of the left vs. right choices was assessed by calculating the normalized entropy U of all sequences of 5 consecutive responses occurring within the total of 100 responses of a condition, according to the formula

p_i p_i

=−i S

∑

∈ ^log²

where S designates all sequences of 5 left/right responses produced by the subject, and p is the probability of each combination i (Hopkinson and Neuringer, 2003). The resulting U-values are bound between 0 and 1, with higher values indicating a higher degree of randomness.

Functional imaging with MEG

Figure 1 shows a fl owchart of the applied analysis procedures. Neural activity during decision making was reconstructed in space and time by using a time-frequency beamformer technique (Dalal et al., 2008). An adaptive spatial fi lter (Sekihara et al., 2001, 2002, 2004, 2005; Vrba and Robinson, 2001) was used to estimate, at each volume element (voxel) in the brain, the mean oscillation power change across trials between a given active and a baseline time window and within a given frequency band. Separate fi lter weights were calculated for each time window of 200 ms duration and for each frequency band. Active windows were shifted in overlapping steps of 50 ms from 0 to 1000 ms after option presentation, and each was compared to a baseline period ranging from 200 to 0 ms before presentation of the options, during which the par-ticipants attended to a central fi xation cross. A power increase in the frequency bands 60–90, 90–120, and 120–150 Hz was used as an index of neural activation. Lower frequency bands (8–30 Hz) were explored in single subjects but not used for the main analyses, because the polar-ity of power changes was ambiguous in these bands. Whereas central and parietal areas showed activation induced power decreases up to the gamma band, activation in frontal and temporal areas was sometimes Table 1. Stimulus conditions and behavioral results.

C. No. Condition Targeted neural Instruction Example Reaction Response

systems time (ms) randomness

1 Visual Shape processing Select the number that 33 634 ± 106 0.94 ± 0.02

is printed in bold

2 Numerical Shape processing, Select the number with 37 545 ± 47 0.95 ± 0.02

numerical processing a higher numerical value

3 Explicit Shape processing, Select the number that 37 731 ± 85 0.95 ± 0.02

Memory explicit memory you have previously memorized

4 Preference Shape processing, Select the number that 37 715 ± 100 0.93 ± 0.03

self-representation you personally prefer

5 Free Shape processing, Select among 2 exactly 33 705 ± 81 0.91 ± 0.05

internal selection identicle numbers

Ten healthy subjects were asked to choose between 2 visually presented single digit numbers, according to 5 different choosing criteria, i.e., conditions.

Twelve to 24 hours before the recording session, they received a list with 5 single digit numbers to memorize and remember during the experiment for the Memory condition. According to their choice, the subjects pressed 1 of 2 buttons with their right hand after the reaction times indicated in the corresponding column (mean ± standard deviation). The randomness of left-right response sequences produced in each condition is shown in the extreme right column (normalized entropy, mean ± standard deviation on a scale ranging from 0 to 1).

associated with power increases in the beta band. Moreover, the neigh-boring frequency bands 30–60 and 150–180 Hz were found to yield no additional information in the average maps of all subjects and were therefore not included in the main analyses. A multiple spheres head model was used. Based on the coregistered structural MRI, individual data was spatially normalized to standard MNI (Montreal Neurological Institute) voxel space at 5 mm resolution.

Frequency bands. Since there was considerable inter-subject variability in the frequency bands that were activated at a given voxel, all 3 high-gamma bands analyzed were combined to a single time-series as fol-lows. For each subject, the time-series of each frequency band at each voxel were tested against the null-hypothesis of 0 power change by using right-tailed t-tests for one sample. Parametric statistical tests were cho-sen for this analysis step, since the log-transformed power change values at a given voxel of a given subject were approximately normally distrib-uted. Right-tailed hypotheses were used, since we statistically tested for activations in the combined 3 frequency bands and therefore had to ensure that all bands showed power changes with the same polarity.

Left-tailed hypotheses would have tested for greater activations during the baseline time window, which was not the interest of our study. A Bonferroni correction was applied at each voxel and time window to cor-rect for testing multiple frequency bands (a corcor-rection for multiple voxels and time windows is applied separately at a later stage, see below). Then, the mean power change of frequency bands showing signifi cant high-gamma activity was calculated for each voxel and at each time window.

Voxels that did not have signifi cant power changes in any frequency band were set to 0. This method yielded comparable results as when calculat-ing the mean of all frequency bands and as when uscalculat-ing only one broad high-gamma frequency band. However, the statistical method introduced here was more sensitive, especially at central and parietal brain regions where beta-desynchronization sometimes extended up to the high-gamma band and interfered with high-high-gamma activity.

Analysis across subjects. The signifi cance of the obtained time-series of power change in all high-gamma frequency bands was tested across subjects with statistical non-parametric mapping (SnPM) (Singh et al., 2003). SnPM does not depend on a normal distribution of power change values across subjects, and allows correcting for the familywise error of testing at multiple voxels and time windows. At each time window,

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average and variance maps across subjects were calculated, and the variance maps were spatially smoothed with a 20 × 20 × 20 mm Gaussian kernel. From this, a pseudo-t statistic was obtained for each voxel and time window. In addition, a distribution of pseudo-t statistics was also calculated from 2^N permutations of the original N datasets (subjects). Each permutation consisted of two steps: inverting the power change values of some subjects (with 2^N possible combinations of nega-tions), and fi nding the current maximum pseudo-t value among all voxels and time windows. Instead of estimating the signifi cance of each permuted pseudo-t value from an assumed normal distribution, it is then calculated from the position within the distribution of these maximum permuted pseudo-t values. The comparison against maximum values effectively corrects for the familywise error of testing multiple voxels and time windows. Power change values were thresholded at a cor-rected p < 0.05.

Contrasts between conditions. A statistical comparison between con-ditions was performed by subtracting the power change values at each voxel, time window, and frequency band. This corresponds to a double contrast (active1 – baseline1) – (active2 – baseline2). The difference maps were then subjected to the same statistical procedure as used for the activation maps.

Software. Spatial normalization was done with the toolbox SPM2 for Matlab (http://www.fi l.ion.ucl.ac.uk/spm/software/spm2/). The algorithms for SnPM were adapted from the toolbox SnPM3 for Matlab (http://www.

sph.umich.edu/ni-stat/SnPM/). All analysis algorithms are implemented in our Matlab toolbox NUTMEG (Dalal et al., 2004), which can be freely downloaded at http://bil.ucsf.edu. Three-dimensional renderings of the activation maps were created with the freely available program mri3dX written by K. D. Singh (http://imaging.aston.ac.uk/mri3dX/).

Data driven region-of-interest (ROI) analysis

Spatiotemporal activation peaks within the 4-dimensional, averaged, and statistically thresholded activation maps were defi ned by repetitively mov-ing the center of an observation sphere with 10 to 20 mm radius to the maximum value within the sphere’s boundaries and within the entire time range until the sphere remained stable. Peaks of all 5 conditions were then manually assigned to common ROIs, if they were located in the same functional area and within a radius of less than 20 mm. The center of a ROI was defi ned by the centroid of all its peak locations, and the radius was defi ned as 5 mm greater than the distance from the center to the farthest peak of a given ROI (15 mm at minimum). The neural activity of a ROI was calculated as the mean power change in dB across its voxels.

Additional statistical analyses

A one-way ANOVA was used to test response latencies, U-values, and ROI time-series for differences between conditions. The statistical probabilities of differences between ROI time-series were corrected for testing multiple time windows with a false discovery rate of 10% (Genovese et al., 2002).

RESULTS

Behavioral data

The mean response time of all subjects and of all 5 experimental condi-tions is shown in Table 1. There was a signifi cant difference between conditions [F(4,45) = 8.0, p < 0.0001], but in a pairwise comparison only the Numerical condition differed signifi cantly from other conditions (p < 0.05, Tukey-Kramer HSD). The left/right responses showed a high degree of randomness for all conditions (U-values >0.9, Table 1), with no signifi cant difference between conditions [F(4,45) = 1.5, p = 0.21].

Spatiotemporal reconstruction of neural activity

Active brain areas (relative to baseline) and their time courses were recon-structed from increases in MEG oscillations in the high-gamma range, Figure 1. Flowchart of study paradigm, applied signal analyses, and

statistical procedures. Example images from the Preference condition are shown. (A) Ten healthy volunteers performed visuo-motor, two-alternative, forced choice tasks as shown in Table 1. Oscillation power changes were calculated relative to a baseline before presentation of the options for a series of moving time windows after presentation of the options, and for each of the frequency bands 60–90, 90–120, and 120–150 Hz. (B) A set of adap-tive spatial fi lters was used to reconstruct the sources of power changes at each voxel, time window, and frequency band from the recordings of all MEG sensors. For contrasts between condition pairs, the difference in power changes between the conditions at each time-frequency point was used:

(active1–baseline1) – (active2–baseline2). (C) For each voxel, the frequency band(s) that showed signifi cant power increases across time were selected and averaged. (D) The spatially normalized power change values at each voxel and at each time window were averaged across subjects and (E) statis-tically thresholded with statistical non-parametric mapping (SnPM).

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5 between 60 and 150 Hz, using an adaptive spatial fi ltering technique

(Sekihara et al., 2001, 2002, 2004, 2005; Vrba and Robinson, 2001).

All decision conditions elicited complex spatiotemporal brain activation patterns, the dynamics of which are shown in real time in Video S1a–e (supplementary material can be found at http://www.mrsc.ucsf.edu/

∼aguggis/frontiers/suppinfo.html). Video S2a–e presents the same data in 30× slow motion, and with the anatomical locations of all spatiotemporal activation peaks labeled. Figure 2 gives a static overview of the cortical areas with signifi cant activations during different decision stages and in each of the 5 conditions, and shows that, as expected, numerous and spa-tially distributed neural systems are required for choosing tasks, including primary and associative visual areas, prefrontal areas, the limbic system, the parietal cortex, the motor and premotor cortex, and the SMA. The spa-tiotemporal pattern of neural activity during decisions was also visualized by defi ning ROIs from activation peaks in each of the conditions, and dis-playing the resulting average time-series of each ROI in Figure 3.

All conditions fi rst induced activation in the medial occipital cor-tex (MOC) of Brodmann area (BA) 17/18, peaking 200 ms after visual presentation of the options. With the exception of the Numerical condi-tion, all conditions also showed activation around the temporo-occipital junction (TOJ; BA 19, 37, 39, and 22) and the occipito-parietal junction (OPJ; BA 18, 19, and 7) peaking within 250 ms after stimulus presen-tation. In contrast, the Numerical condition elicited an early activation of the right dorsolateral prefrontal cortex (DLPFC; BA 6). Additional early activation peaks (∼200 ms) could be found in the right superior parietal lobule (SPL; BA 7) during Memory judgments, and in the left supraparietal lobule/intraparietal sulcus (SPL/IPS; BA 7) as well as in the left cerebellar (Cb) hemisphere during Free decisions.

Subsequently, various brain regions showed an activity peak dur-ing a time window between 250 and 500 ms after option presenta-tion, depending on the choice condipresenta-tion, suggesting a second stage of choice processing. All conditions elicited activity peaks in supplementary motor area (SMA; BA 6) and right motor cortex (MC; BA 4 and 6), as well as reactivations of TOJ and OPJ, with no signifi cant intensity differ-ences between conditions in these regions during this stage. Moreover, additional specifi c activations were observed in the Visual, Numerical, Memory and Preference conditions. In the Visual condition, the superior parietal lobule/intraparietal sulcus (SPL/IPS) was activated on both sides;

in the Numerical condition, activations were seen only in the left IPS. The Explicit Memory condition was associated with activation in right DLPFC (BA 6 and 8). During the Preference condition, activations were observed in cortical midline structures progressing from medial parietal cortex

(MPC; BA 7 and 31) to supragenual anterior cingulate cortex (SACC, BA 24 and 32; see Video S2d), as well as in left DLPFC (BA 6 and 8). The Free condition showed reactivations of the left cerebellar (Cb) hemisphere and the left SPL, as well as activation of the right posterior inferior parietal lobule (IPL; BA 39).

In a third stage, (re-)activations of parietal and occipital brain areas peaking between 500 and 600 ms after presentation of the options occurred, with each condition being associated with a distinct subset of parieto-occipital regions (Figure 2, Video S2a–e): the Visual condition with the MPC (BA 7) and the left IPL (BA 40); the Numerical condition with the MPC (BA 7); the Memory condition with the right IPL (BA 40), left SPL, and left OPJ; the Preference condition with the left IPL (BA 40), left SPL, and bilateral OPJ; and the Free condition with the left IPL (BA 40) and the right SPL/OPJ (BA 7 and 19). Furthermore, the Cb showed activation peaks during this time window in all conditions. During preference judg-ments, the activity in the cortical midline structures migrated further in an anterior direction, and induced a peak in the pregenual anterior cingulate cortex (PACC; BA 32), as visualized in Figure 3.

Finally, activity in the left (contralateral) premotor and primary MC that has been building up during the second and third processing stages converges in the precentral gyrus (BA 4) and peaks between 550 and 650 ms after stimulus presentation in all conditions, about 50 ms before the average button press latency of the subjects (Table 1). During and immediately after the button press, the Preference and Free conditions were additionally associated with reactivations of the SMA, the left SPL/

IPS, and the SACC (Figure 3), which were signifi cantly greater than in the other conditions (p < 0.05, corrected). Ventromedial prefrontal cortex (VMPFC; BA 11) was signifi cantly activated only during the Free condition, and only after the activation peak in the motor cortex (Figures 2 and 3, Video S2e).

The MNI coordinates of all spatiotemporal activation peaks are indicated in Table S1 (http://www.mrsc.ucsf.edu/∼aguggis/frontiers/

suppinfo.html).

Contrasts

In order to better isolate the neural networks being specifi cally involved during the different choosing conditions, we additionally performed four planned statistical comparisons between condition pairs. The Numerical, Memory, Preference, and Free conditions were each contrasted to the Visual condition, since we hypothesized that the former four condi-tions each relied on one or several neural systems in addition to visual shape processing required for the Visual condition (Table 1). Figure 4 Figure 2. Spatiotemporal reconstruction of synchronized high-gamma activity during choosing. Average activation maps of 10 healthy subjects are superimposed on a 3-dimensional template brain. Colored brain areas show the reconstructed sources of signifi cant (corrected p < 0.05, SnPM) high-gamma activity during the 5 task conditions. Rows represent different stages in the decision process.

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Figure 3. Time course of induced high-gamma power in regions of interest (ROI). ROIs were derived from spatiotemporal activity peaks in all 5 choosing conditions. Occipital areas showed the fi rst activity peaks, but were also reactivated later in the decision process. The DLPFC contributed to preference and memory judgments during the option evaluation stage of choosing. Activity in cortical midline structures was most prominent during the Preference condition, and progressed over time from posterior to anterior parts. Parietal regions showed consistent activity peaks during the intention stage of choosing, with the intentions being mediated by a distinct set of posterior parietal and superior occipital subregions for each condition. Activity in the bilateral MC started to build up immediately after visual presentation of the options, and peaked roughly 50 ms before the average behavioral button press latency. High-gamma power

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